Clothing manufacturers and merchants currently use a broad range of methods to show clothing size and fit including, but not limited to: mannequins, models, manual measurements, and standard clothing sizing indications. However, each of these methods have limitations and new methods that minimized and/or eliminated these limitations would be beneficial to manufacturers, merchants, and consumers. For example, standard clothing sizing indications (S, M, L, XL, etc.) are generic and do not reflect specific dimensions sought by a consumer. Clothing sizing and measurements also vary greatly throughout the clothing industry. The process of sizing and measuring clothing is not sufficiently standardized in the clothing industry, such that clothing sizing can exhibit significant fluctuation between clothing brands. Moreover, fluctuation in clothing sizing can even occur within a single manufacturer, due to changes in clothing styles/patterns, poor quality control, poor manufacturing practices, etc. Given the wide variability among clothing sizing that currently exists, consumers are often required to personally try on each prospective article of clothing to ensure a proper fit before purchase. This process can be time consuming and frustrating for consumers and requires a physical storefront for merchants.
As online clothing sales continue to grow in popularity, a more modern and accurate method of determining clothing fit and dimensions which requires little or no extra effort from the merchant and the customer would be desirable, as it is estimated that 25% of all online clothing purchases are returned. However, a simple and effective method for determining accurate dimensions and fit of clothing online does not currently exist.
Clothing returns due to improper fit cost online merchants in many ways including, but not limited to: time costs to package items for shipment, time costs to process returned items, time costs to review and restock returned items, time costs to process refunds and/or exchanges, packaging and shipping costs, employee costs to handle returns, etc.
Systems and methods that would allow customers to provide photographic evidence to online merchants of clothing items that have shrunk, stretched, were improperly manufactured or mislabeled (e.g. mislabeled sizing), etc., can help the online merchants validate these returns and help the online merchants defer the costs of these returns to the manufacturers to incentivize the manufacturers to make appropriate production changes to fix these issues.
Clothing returns due to improper fit also cost consumers in many ways including, but not limited to: time costs to request and process a return with the online merchant, time costs to package the item to be returned, packaging and shipping costs, etc. Clothing returns also negatively impact the environment by increasing the carbon footprint of online merchants and customers by wasting resources necessary to return clothing items that do not fit.
Clothing manufacturers and online merchants can also benefit from a system that would allow them to view anonymous customer clothing data to help them verify that the dimensions of their clothing items are meeting the customer's demands. For example, customer generated clothing images and dimensions that are accessible by manufacturers and online merchants may be helpful to ensure that clothing items are sized to fit a majority of their customers, may help allow merchants to select which clothing they wish to sell, and/or allow manufacturers to identify niche clothing markets that are underserved. Customers can also benefit from a system that would allow them to accurately compare clothing sizing between clothing they already own and like and potential online clothing items. Moreover, made-to-measure/bespoke clothing manufacturers can also benefit from a system that would allow them to accurately ascertain clothing dimensions from their customers without the need for manual measurements, as manual measurements are error prone.
Systems and methods that would allow customers to quickly determine if a prospective item of clothing will likely fit them by visually inspecting the prospective item of clothing with simultaneous reference to an item of clothing of known fit, would also be desirable. Such systems and methods would provide more accurate sizing information about particular articles of clothing over traditional sizing indications/nomenclature (e.g., S, M, L, XL, etc.) currently used in the industry.
Although the present disclosure utilizes clothing sizing comparisons as the main example of illustrating the sizing comparison process between two or more objects, it will be understood that the computer vision sizing comparison concepts and program described herein may be utilized in any scenario where sizing comparisons between two or more objects may be made. Thus, example uses of the sizing comparison program and concepts that are taught in the present disclosure are virtually endless. Some additional examples of use scenarios for the sizing comparison program and concepts that are taught in the present disclosure may include, but are not limited to sizing comparisons of: shoes, hats, gloves, plates, silverware, placemats, cutlery, air filters (e.g., home, auto, small engine, etc.), pillowcases, blankets, curtains, wash cloths, towels, mobile phones, tablets, laptops, photo frames, books, paper items, shelves, rugs, wall art, clocks, mower blades, wrenches, tools, etc.
Moreover, although the present disclosure mainly describes sizing comparisons and/or measurements between two or more objects, it will be understood that that the computer vision concepts and program described herein may be utilized to make size measurements of single objects alone.